Empowered by intelligence: Women, AI, & the future of Finance

Hello everybody, welcome to today's webinar. We're so glad to have each of you join and spend some time with us today. We're going to wait another minute or so to let some more people roll in and then we will kick off in another minute or so. Thank you.

Okay folks, welcome once again to today's webinar. We're really, really happy you could join us today on what is a late Wednesday in February.

Today's webinar is entitled Empowered by Intelligence, Women, AI and the Future of Finance. To give some background, at OneStream our goal is simple, to take finance further. And that's not just about delivering transformative technology for today's finance teams, it's also about investing in the next generation of finance leaders. One of the ways we bring that to life is through OneStream Women in Finance.

It's a community built around connection and conversation. A place to share experiences, exchange practical insights and support one another as we grow, stretch and lead. Today's conversation is grounded in insights from our Glass Chair research, which captures the real experiences of women in finance, the challenges they're navigating, the opportunities ahead and the progress that's already being made.

And that brings us to today's webinar theme, the tech pioneer. We'll explore why AI and digital fluency are quickly becoming essential skills for future finance leaders. How skills and generational gaps are shifting and how women can step forward to lead AI adoption, driving greater impact, greater influence and real career acceleration along the way.

Now that we've set the scene, it's time to hear from some voices who are actively shaping what it means to be a tech pioneer.

Thank you so much for each of you for joining us today. Starting with Shikha, would you please introduce yourself and share why you're excited to be part of today's webinar?

Great. Thank you. My name is Shikha Gandhi. I'm a CPA CA located in Toronto, Canada. I'm also a Six Sigma Greenbelt and a UC Berkeley certified executive coach. I started my career with Deloitte a very long time ago with a focus on transaction advisory. From there, I spent some time in a traditional controllership role at a large Canadian courier company called Purelator, where I quickly transitioned from controllership to finance modernization.

Currently, I serve as the CFO at Carina Software Group, which is a division of Constellation Software, a publicly listed company in Canada. I had the opportunity to meet some of the wonderful ladies on this call last week and hear from them on insights and opportunities for women leading this AI revolution. I'm excited for all of you to hear what they have to say as well.

Thank you so much. Tiffany, we'll go to you next, please. Thanks, Aisling. Yeah, so I'm Tiffany Ma. I'm the Director of Product Marketing for AI and Operational Analytics here at OneStream. I have been in the CPM space for a very long time. I started at Deloitte, just like Shikha, implementing CPM tools. I eventually ended up in OneStream, continuing to implement. I moved to pre-sales, and now I'm in product marketing. I am very excited to be here today to join Shikha and Emma to just really learn from their different perspectives and experiences and just have a great conversation today.

Thank you, Tiffany. And now I'll move to you, Emma. Not in finance, but definitely AI adjacent, I think that's fair to say.

Hope we got you, Emma. If not, we'll jump into our topics and we can circle back. Okay, we'll jump into our first topic and hopefully we can reconnect with Emma. The first topic we want to look at is AI as a differentiator. Let's start with why AI and digital fluency are no longer optional for future finance leaders.

Digital fluency is quickly becoming a real advantage and not just a technical skill, but a leadership capability. Our finance 2035 research showed that most CFOs expect AI and automation to fundamentally reshape finance.

And many CEOs believe that organizations that don't invest in technology and skills today may not survive the next five years. So this is a moment. It's a moment where women can step forward and lead. Women in senior finance roles see digital capability and leadership skills as essential for growth. And tools like AI and automation are increasingly at the heart of better, faster decision making. But here's the challenge.

Most organizations simply do not have enough finance talent trained in AI and closing that gap is critical.

Moving back to the panel, AI adoption is moving fast, but it looks like our leadership mindsets haven't fully caught up. So what does digital fluency really mean for the next generation of finance leaders, especially for women stepping into that future? Tiffany, can you kick us off?

Sure, Aisling. Thanks for that context. So before we get into specifically digital fluency, I wanted to just give some background in where we are today and how AI has really accelerated lately in its pace and development and how it's evolved. So if we think back, we start off with software engineers who are writing code to automate repetitive human tasks. And then over the last year, we've recently seen now AI is writing the code. And this is where things really started to accelerate dramatically. So now code is writing code itself.

And now we're entering into the agentic era where autonomous AI agents aren't just responding to prompts. They're planning, they're reasoning, they're able to call tools and iterate and really execute on multi-step tasks.

And so again, the pace of acceleration with AI is now helping to build better AI and much more quickly. And what this means is we're not just automating tasks anymore. We're beginning to really automate decision workflows. And this is a critical step change. And so instead of having to automate individual steps or tasks, we're now automating entire systems from data ingestion to multiple scenario simulations to recommendations for corrective actions and trigger alerts or initiate approvals.

And so going back to your question, Aisling, digital fluency is really critical and being able to understand how to leverage these new and very rapidly evolving agentic capabilities to get the most value out of them is really how I think of digital fluency.

Fantastic. Thanks, Tiffany. Same question for you, Sheikha. What are you seeing? How would you think about digital fluency from your world and what you're seeing so far?

Sure. Thank you for that question. I was reading a recent study that suggested that there's a 25% adoption rate differential between males and females adopting AI. And I thought that was really interesting. And when I read further into it, it talked about, you know, women might be hesitant because of moral, philosophical, environmental objections. They might be worried about AI, just lack of knowledge, lack of fear or because of fear.

And I found that really interesting because I think what we are maybe discounting is that we can use the skills that we have today that we use every day in our day-to-day careers, in our day-to-day work and use that to make ourselves digitally fluent. And all that really means to me in my career is asking better questions and remaining curious. And that's no different to me being in an AI-enabled world than it is, you know, 10 years ago in my career.

I think it's a sweet spot for women, the curiosity. We don't have to be technical anymore. We don't have to be the loudest voice in a room. We just have to be critical thinkers and bring some clarity in our question asking. I also think it means that we understand data flows and infrastructure. So, you know, data, whether it's online or offline, has to get from A to Z. And we have, we are positioned to bring clarity to that data flow.

And lastly, I think that, you know, maybe I'm generalizing, but women tend to be really great collaborators. And now we're going to be asked to collaborate in a hybrid environment. So that means collaborating with people, but also collaborating with machines, and kind of building that connectivity between people and machines. So I think that's what it means to be digitally fluent.

Wonderful. Thanks so much, Shinka. We're going to turn, we're going to go back a little bit, because Emma, we missed you first time around. And then we'll jump to the question. But Emma, if you wouldn't mind sharing with this audience today, a little bit about your background, and, you know, why in particular, you're excited about this webinar.

Yeah, and thanks, everyone. Apologies for the for the technical issues there. So very quickly, my name is Emma Redmond, I'm Associate General Counsel, I head up privacy and data protection on the legal team for OpenAI. But I'm also heading up OpenAI Ireland.

I was the first employee for OpenAI here. And so it's expanded quite a bit since then, which which is which is fantastic. And I'm a junk professor in UCD Law School. And I'm also a member of the government's AI Advisory Council, where literally helped to advise government on on AI policy. In terms of career, started off actually as as a barrister in court went from there straight to US multinationals for the past 20 years, and starting at AdTech, moved to LinkedIn, of course, actually, you and I work together.

And, and from there, of course, to to ancestry stripe on the on the on the financial side, financial payment side. And of course, now, at OpenAI. So it's been an incredible journey, and one of which I'm very, very grateful for. And in terms of I'm excited about, I mean, you can hear already, just, you know, what's been spoken about in terms of the opportunity and the leadership, they're all key words that we're using, but they are, they are in no way, or should be undermined in any way. They're so fundamental in terms of the opportunities that we actually have here in this space.

I think what I'm also excited about is as I'm looking at the chat, I hear I'm seeing so many people from all over the world, listening to this. And I always say, if you can get, you know, one aha moment from an event, then it's definitely a successful one. So I'm looking forward to that.

Thank you so much, again. And so we might flip back then to the digital fluency question, which we kind of started looking at and hearing the themes from Tiffany and Shika, we're, we're talking about curiosity, we're talking about hybrid motions, we're talking about being like the transformative aspects. So, you know, maybe obviously you're not in the finance space, which most of our audience will be, but I made a little joke and you missed it earlier, Emma. I said, you are definitely in your role in open AI, AI adjacent.

So you are definitely a worthwhile panelist. But when you think of digital fluency, you know, what does that mean as a concept for you?

Yeah. Well, first off, I feel like I'm a semi finance person, given I sit beside finance in most of my organisations I've been in. So I feel I'm an honorary member. But look, it means different things to different people. And I always say it's important to, you know, context is king in terms of what digital fluency means for you and for your organisation. To me, it goes beyond, though, being able to use technology.

It's actually about understanding how technology, how this will reshape how you think, how you decide, how you're going to lead.

And I think in finance, it's very apparent, right? Because it's, you know, AI is already embedded in models, in forecasting, risk analytics, customer interactions, you know, you name it.

But what often lags behind is that leadership mindset, because you can't have one without the other, the ability to, I suppose, integrate technology into strategic decision making is critical. And I think that's where everything steps up a whole other level.

For the next generation, I think of finance leaders, from what I can see in my liaising with with finance teams, is that it means one strategic translation, right? So that means I see it as connecting data and AI insights into real world business outcomes, like literally real world business outcomes. The second is that judgment of curiosity that was mentioned earlier.

So knowing when to say, yeah, that's automation, and then very critically to understand when to question it, and how to question it. I think that's really critical.

And of course, throughout all of this, you have to have the human at the central of all of this in terms of in terms of leadership.

What resonates for me as well is something that Sarah Fryer, our CFO at OpenAI has emphasised. And she talks about digital, I'm sorry, technology fluency, not in terms of just the tools, but as leading through technology. So that's actually taking it up, as I said, a whole other level again.

So again, like, it's not enough to understand the systems functions, you have to understand why it matters, you know, how it shapes things, how it shapes customers, how it shapes your community. And that's why I go back to saying, you know, context is king in this, and that will help determine what digital fluency means for you.

I love it. Lots to digest already there, Emma, even in terms of the context of king, the real business outcomes will resonate again with many of our audience, given that that is how we spend our time thinking about the future of our financial orgs, what different decisions mean for our businesses, the leaderships we support.

And I want to jump quickly back to a piece you mentioned around, I think you mentioned that a kind of adoption from a gender point of view. But, you know, we've kind of got two realities coexisting here, in terms of that, like, in general, outside of adoption, but women are underrepresented. And in the research we've done, women are underrepresented at leadership.

And at the same time, because of this idea of AI as an opportunity, as an accelerator, that opportunity has never been greater. So, you know, we've kind of come through different waves from in any of our careers. So what makes this particular moment different from prior waves of digital transformation for women in finance?

That's a great question. Thank you. So as I reflect on my career, I've been privileged enough to work in places, even today, where women are at parity with men in leadership roles and finance.

But we definitely are underrepresented. There was a study done, I think, in 2015, where they looked at the number of women CEOs on the FTSE 350 and the S&P 100. And there were more CEOs named John than there were women CEOs, which blows my mind. But that didn't, we didn't reach, forget parity. We didn't, we didn't get to a point as women where women outnumbered CEOs named John until 2023. And that doesn't mean like, forget the Andrews and the Michaels of the world. We're just, we're just at parity with the Johns of the world.

But what does that mean? We're in this era now where data is, is being democratized. And so everyone has access to loads of data they wouldn't have had before. And women haven't been in the room where it happens for that long. And so we're really used to working with imperfect information.

And so now we have all of this data that is compiled into, again, into analysis that we then have to question. And so to me, the opportunity for us is being really good at questioning incomplete or inaccurate data, and communicating what that then could mean or should mean to a business across all functions. So, you know, we, we have an ability to meet people where they are and be able to bridge a gap between finance and sales and R&D. And you name the function, and we can be that bridge that brings information to them at a level that that is relevant to them.

And again, it goes back to my comment around being able to influence without necessarily having power. So we can be that voice in a room to bring it all together.

It's about about change management. We're good at reinvention. We're good at experimentation, because women, again, have been doing it for, for as long as I've been in the workforce, and well beyond that. And so I think our opportunity now is to leverage the skills that we've developed now that we are invited to the table and are in the room and transforming that into something that revolutionizes in the digital age.

Wonderful. That transitions nicely into our second topic. So our second topic is looking at skills, generations, and those gender gaps. So let's explore how demographic and skill shifts are influencing the future of finance leadership. We're definitely seeing some clear gaps across the industry. There is a skill shortage. Many finance professionals don't yet feel confident using AI, and there's also a generational divide, with younger talent feeling more prepared than more seasoned professionals.

And so while 73% of women want to develop AI skills, fewer feel confident using AI compared to men. This is not a capability gap, though. It's a confidence gap. So let's explore together how we close that gap and build the confidence to lead. Shikha, I'm going to come back to you again. Again, our research is looking at 70% of CEOs believe in companies that don't invest in technology may not survive. What does future proofing mean for finance professionals today? Thanks. All these great questions.

So to me, again, I'm going to go back to what I've talked about a little bit already around having comfort with completeness or incompleteness of information and being comfortable extrapolating what that information could mean or should mean to an organization. And I think sometimes we get caught up. I know I get caught up in wanting complete information before making a decision. But with AI comes speed.

So we suddenly have data that comes together to form information much more rapidly than it did before. And so I think part of future proofing is becoming really comfortable with interpreting incomplete data and extrapolating what that could mean to an organization.

So that's sort of number one. I think number two goes back to my comment on leading in a hybrid work environment. And so again, we are great mentors and coaches.

And so we have to be able to mentor and coach in this machine and human environment, which can again present a little bit of discomfort.

And then lastly, I think we need to become really great storytellers. So it really is about telling that story and bridging the gap.

And so what it all comes down to, I think, is being confident in in our storytelling abilities and bringing people together. And that doesn't sound very technical at all. And it shouldn't be right because we no longer have to be technical leaders to live in this AI enabled world. We need to be interpreters of incomplete information. We need to be the bridge that brings financial information to the rest of our organizations.

And we've always done that. So I think part of the fear is a technological fear. And if we can get over that and kind of lean into the things that we've always been good at, that really would help us future proof.

I like that because it resonates to your point. These are things that we've always already done. We have to be storytellers.

We have to bridge data with insights, with decision making. So it's the underlying skillset, it's the underlying themes of being financial leaders, but then it's layering on this whole other level of capability and kind of how the future of work is going to look. A slightly different theme for you, Emma, but really, really relevant and will be relevant to this audience, given our kind of financial background and how we think of data.

But I mean, I suppose we're seeing that AI adoption is accelerating faster than most governance and legal frameworks. So from your perspective, how should finance leaders balance speed, trust, and accountability as AI becomes embedded in decisions throughout the company, but for this audience, financial decisions?

Yeah, I mean, we have never seen the pace of, well, AI acceleration before at this rate, or, but certainly on the regulation side. So aside from being good friends with your legal team, who are grappling with quite a lot of issues and a lot of themes and regulation right now, I do think it's important sometimes there's a lot of different issues going on at once. As you mentioned, Aisling, you know, the speed, you know, trust, the accountability, like again, all these critical words and keywords that we're using all the time.

But at the end of the day, I find that it's it's so significant and profound to be able to go back to the basics at all times. And sometimes, you know, I go back to, you know, GDPR often every day, it's what I practice in every day. And I look at that being, you know, principles based piece of legislation and what that is intending to do. What is the intention? What's the purpose behind it? And when you go back to that, and you trim it down to its basics, if that can be your guiding star, the whole way through, I think it stands to you very, very well.

In terms of, from the finance side, you know, you're also grappling with that. And, and I always say that it is, you know, a case of going back, as I said, going back to the basics all the time, going back to the fundamental questions, what's the problem I'm trying to actually solve for, because that will, but it sounds so simple.

But I've seen successes and fails around that question alone. And it's, it's, it's asking the right questions that will actually dictate how fast you go on something, or how fast you need to go, the trust aspect, and also the accountability. I think there's a few, you know, things to consider. The first is for finance leaders, and trying to, you know, balance that is, you know, you're looking for clarity of ownership, right?

AI systems aren't going to remove responsibility, it redistributes it, right? So as leaders, you need to be explicit about who is accountable for what, for model validations, for data integrity, whatever it may be. The second is risk-based deployment, right? So, you know, again, I go back to the point, context is king. In what situation are you using this, this kind of deployment? For what purpose? Why?

And that goes back to the regulation, because that's what the regulation is asking for all the time.

And then the third is this, this notion of transparency. We can never underestimate how critical transparency is as a strategic asset and as a leader. Because, you know, if, if, if, if your leadership here is going to be built on being open, on how something is being used, finance leaders really should be asking, can we explain this decision? Can we evidence data sources? Can we demonstrate fairness, auditability, like asking all of those questions? Now, having said all of that, speed matters, of course it does. And it matters more and more.

But, but the trust compounds, for sure it does. And I think having that accountability and having that transparency is, is the way in which you can actually balance all of those elements, as well as thinking of the context and going back to basics and the fundamentals.

So look, I'm not going to sit here and say it's, it's straightforward and you follow this formula and off you go. It is dependent on the situation.

But I think those elements, as I mentioned, transparency, the accountability piece, that ownership piece, you know, the risk-based deployment and going back to the basic and fundamental questions, you know, you can't go far wrong in those situations.

I think that's a, that's a really, I think again, this will, will be very applicable to a lot of people on this call, especially who are people who are very, very early on the journey, all the way through to people who are quite along the AI adoption journey. And, you know, that idea of intent and first principles, but you know, it, there's not formulaic, there's no white paper or rule, you know, book of rules that we can follow. But it, I suppose, if you think of how we approach our job already as finance professionals, and how much of it is based on doing the right thing.

And, you know, there's a, not a moral side to it, but when you're a financial leader, you are looking at the black and white of numbers a lot of the time. So I appreciate that answer, giving that, you know, finance, you might have sat beside a lot of finance people, Emma, and we appreciate that.

And moving into our next topic. So, you know, we've looked at, this is kind of looking at confidence versus preparedness. So we're saying, okay, women are adopting, it's not a capability gap, is it a confidence gap? So then how do we move it on into the next phase? So how do we, let's shift to practical strategies for women who want to lead AI adoption and finance. And that's regardless of technical background. We've talked about building confidence. Now the focus is scaling and execution.

Many leaders understand AI in theory, but far fewer are prepared to deploy it across a finance organisation. The difference comes from hands-on experience.

Targeted upskilling and working on real AI initiatives are what turns awareness into real capability. So I'll come to you, Tiffany, first. But this is where ambition meets reality. Finance leaders understand AI, but scaling it is a completely different challenge. What, you know, in your experience, in your role, you're speaking to financial leaders all the time. What's driving that gap from your perspective?

Yeah, I think it's a great question, right? It's the tactical, moving into the tactical area. And I really think a big area element of it is cultural readiness. You know, you mentioned, Ashling, that finance is used to numbers and working in black and white.

They've typically been optimized for precision and control of their numbers. And what AI does is it introduces probabilities and iteration and continuous improvement. And so what does that mean? That means finance needs to move into that from the black and white into the gray area.

And they need to learn to be able to accept and understand what responses from generative AI are actually accurate and what's not, and take that information and be able to make meaningful decisions or take true action, to Emma's point, right? And I think that's, that shift really requires a mindset, mind shift change and a cultural change.

A second more, more tactical, technical area is to be able to operationalize. It really is an area that's important is tool sprawl. So, you know, AI really works best when it's operating from a single trusted source of data.

When you have pockets of disconnected systems or duplicative data sets or, you know, siloed processes, these models, they lose context. And, you know, we've all, you know, I love Emma's quote of context is king, right? Context is critical for AI. Without it really, it's just noise. And so this is so true. And really being able to have, you know, a single source of trusted data is critical to be able to operationalize it.

Fantastic. Shigha, I think the same question for you, because this will be relevant, you know, again, across all groups. We know we want to do it. We know we want to take charge, but, you know, how do we get over that hump? Like what's driving the gap from what you're seeing, you know, in your own company, but maybe in your own network among peers on the gap between, you know, the challenge of adoption on scale?

I think Tiffany and Emma have sort of both in their own ways hit on this already. But I guess in the first instance, I would say, again, context is king. I really like that, Emma.

And to pick the right AI framework for the business that you're in and the finance team that you sit on. Because I think when you look at other use cases for AI outside of finance, it's really clear.

And there's one big use case that's going to, you know, give tons of efficiency. So like an R&D, Tiffany talked about coding and AI now writing code for itself.

And so that use case is really clear. And I think coming up with an AI framework for finance might be a little less clear depending on the sort of stage of maturity the finance team is in, right? So if everything is all manual and data is everywhere, your framework is going to be a lot different than something where data is centralized and you're already on this journey of automation and modernization and finance. So I think that AI framework

is sort of number one. And then number two, Tiffany also hit on this. It's a cultural change. And I think we you know, we were told by leadership to adopt AI or we need to be AI enabled.

And so we look at it as a project and we look at it as sort of like a one time, IT is going to tell me what to use and it'll be an IT led project. But this is, this is, AI adoption should not be an IT led project. It shouldn't be a project. It's sort of, it's a revolution.

And I, and I was trying to think through, you know, the last time we would have seen this kind of revolution. And in my lifetime, it would have been, you know, maybe the internet age. But to me, that's not even big enough. I think, I think if you want to look at history, it's almost like an another industrial revolution, right? We are the industrial revolution, modernized agriculture, it modernized manufacturing, it completely changed the way people were, were working. And so people had to upskill to meet the demands of this industrial revolution. And now we are, we're in an AI revolution.

And so it's the same, same thing around having to upskill and then finding, making sure that the leader, that leaders in your organization are going to support you in that upskilling, I think is really important.

But I, I do believe if we treat this as an IT led project, that upskilling and that leadership support isn't really necessarily there, because it feels more isolated. And so I think that this cultural mind shift that Tiffany talked about is, is pretty key for finance being able to, to close that gap.

Thanks, Shikha. I think we're really kind of upping the ante by talking at talking about this as much bigger than, you know, prior changes we've seen that this is, it's a revolution. This will, this is changing already and will change how all of us operate, work, strategize, make decisions. And that goes beyond just the finance org.

And this will be applicable to every industry, every team, and every vertical. And I think, Emma, you can probably speak to that broader idea, because I'm guessing in your role in OpenAI as the lead in Ireland, and you're seeing that movement from, yeah, you know, teams or companies who are ambitious about adopting and actually then jumping to the reality of adopting.

So have you seen key teams there or key successes for any of those industries that you work closely with? Yeah.

And we've actually just announced our own European SME accelerator program, actually, and it really speaks to that.

I think like it, it's interesting, right? When you, when you think about the gaps that are there, where I'm sitting, where I can see it is that there is such a frenzy with respect to AI. And by that, I mean, there is such an urgency to apply AI to every project and every work title. And that's great. But again, this is where the context is really critical, because it doesn't have to be, and there's almost an anxiety that, you know, this, we need to prove this, we need to apply this immediately.

But this is why it's so important. Again, this is where I've seen the successes and fails is where there's, you know, companies, organizations where they're not asking the basic questions.

And there's something very profound in that, because if you don't start asking the basic fundamental questions, then you're really going to miss a potential success, right? So I always say there's kind of like misuse of AI and there's missed use of AI. And in the frenzy of what is, of what this is, you can actually miss incredible use cases.

And so one of the ways in which I like to look at it is that, look, try and keep things as simple at the start, at least from the beginning. I think the other thing is, this was touched on, relates to cross functionality.

This is not that there is so much to learn from your colleagues. Okay, you're in finance, but there's so much to learn from colleagues and other functions and departments. I know I do in the AI world where you know, they have ideas in terms of challenges that they're seeing. So you can take those, see how it applies to you, the different prompting that people use. I mean, it's incredible what you can learn. And so these kinds of gaps don't have to be the most complex and the biggest of gaps. They can be actually quite simple and basic in a way.

So I would say as well, like look around you and seeing seeing what's happening. And I'm seeing that with companies who are being very successful at this. It's interesting what was discussed earlier in terms of, you know, the internet. And I do agree, like this is, this is different because the internet actually had relatively low capex.

When you look back, this is very, very different. And it is, it is certainly a different revolution.

The other thing I will say that I think is helpful, and it's something that, again, I point back to Sarah Fryer, our CFO, she's fantastic for doing this, which is carrying out hackathons within our own finance teams, different ways in which they can, they use AI and different systems and processes that they create out of it. And they have great fun along the way, but it's hugely rewarding and they get incredible outcomes from it. So, you know, I suppose what I'm trying to say is it's not, it's, it doesn't have to be the most complex. It can be the easy way to start off.

And then you take it from there. Now, having said that, I know that it's easier said than done to go from there to, you know, a real maturity level in terms of AI, right? So scaling is much harder than experimenting, right? It is. But I think what takes you up there is taking it step by step. But also what you need is that data maturity. You need to know what you have, and you work from there. And it's as a result of that, you're able to gradually connect the dots together and see what makes sense, again, based on your context and your sphere and what you're seeing.

I think that was a true case, Emma, of knowing your audience when you mentioned CapEx. You clearly knew you were talking to a finance webinar today. And Tiffany, I'm going to jump back a little bit, you know, in an earlier talk, we talked about gaps. So one of the gaps is generational.

So, you know, again, some of our research is showing that only about half of experienced finance professionals actually feel prepared to use AI compared to much higher confidence amongst students and early career talent. So how should finance leaders rethink upskilling so experience becomes an advantage and not a blocker in this AI era?

Yeah, great question. I actually see experience as one of the greatest competitive advantages in the AI era, if we frame it correctly.

I think, you know, coming out of this conversation, I think the overarching theme is context, right? Context is king to Emma's point.

I'm going to bring it back to the experience piece in that AI is powerful, but it's context agnostic. And again, context is king. So without it, it doesn't understand your business model, it doesn't understand your industry cycles, your regulatory nuances, or, you know, any unwritten assumptions that are embedded in your forecast or whatever finance functions that are occurring. And so where experienced professionals come in is that they do understand all this. And so pairing that domain expertise with that technical fluency is a real multiplier.

And so to rethink upskilling, I think finance leaders really need to shift from formal training programs to embedded learning in real workflows, the tactical element of it. I'm a firm believer to learn any technology, you have to really get in there and get your hands dirty. You can't just learn theory on its own. You have to jump right in, have your employees use AI in as many areas of their life as possible, whether it's professional or personal.

And then we spoke about this earlier in the beginning with digital fluency. I think we have to redefine what AI readiness means. It doesn't mean coding anymore. It means asking better questions, challenging the model outputs, understanding the assumptions that went into the model.

And again, most importantly, you know, I know Shika and Emma have mentioned this, but being able to be storytellers and translating that insight into business action.

And then I think, you know, recognizing the soft skills, more of the soft skills, like being, you know, we talked about this as well, but being comfortable with being uncomfortable. So, you know, what do I mean by this? I mean, pushing outside our boundaries of comfort to learn new skills.

Communication is critical as ever than before with AI, right? Communicating with clear prompts, communicating the results, communicating amongst various stakeholders. I think this is a really strong skill set of women and it's needed more than ever now with the AI era. Fantastic, Tiffany. Fantastic, Tiffany. I think that we're moving on to our fourth and final topic, and then we should get to some Q&A before we wrap for today. So the AI opportunity, and here's the good news. Everything we've talked about today, the opportunity for women in this space is huge.

Even though women are underrepresented at the top, those who embrace digital transformation will shape the future of finance.

By leaning into these shifts, women can modernize finance functions and help guide their organizations through impactful digital change. And that's exactly what today's conversation is all about. Confidence, capability and leading the future of finance.

Shika, coming back to you, the research shows that 74% of CFOs believe AI will fundamentally transform finance by 2035. Yet readiness lacks ambition. What do you think leaders most underestimate about what it takes to be AI ready?

Thanks for that. I'm going to go back to my earlier comment on, I think because we've been so encouraged, I think Emma said it, there's this frenzy around AI and AI adoption.

And so I think we're treating it like a project. And so I think where what we're underestimating is really the change management that needs to underpin the adoption of AI.

And what do I mean by that? I mean, we need to stop treating it like a project. And we really need to think about asking the right questions on, you know, how do I transform where my data sits? How do I get access to that data? What does my workflow look like today? What question am I trying to answer with AI? And will that question be more quickly answered with AI? Who needs this information? Where do I need to go? And so it's it's this combination of treating it like a more transformative outside of an IT project, more transformative. I guess.

Issue and and then using the skills that we have in asking those critical questions to kind of help get us AI ready, because if we can't answer that question today on where data sits and how the workflow works, it doesn't matter what AI tools you introduce to your finance team.

You're not going to get the yield or the outputs that you're hoping for. So there's some really fundamental things that that need to happen today that I think all of us on this call can play a part in understanding our current state and understanding what the future state needs to look like so that we can actually bridge that.

Thank you. I think we're hearing that as a recurring topic. It's, you know, again and again, this is not just a siloed IT project. This is a whole I'm going to use your words again. It's a revolution. It's cross company. It goes beyond the companies we're all working in. Tiffany, kind of in the same pattern, but how can women position themselves as those pioneers of digital change?

Yeah, that's a great question. I think, you know, I've touched up on it before. And so have Shika and Emma. It's just leveraging your own strong suits, right? Communication was mentioned as our strength. So being able to translate the AI outputs into insights, decisions and actions.

Shika mentioned collaboration as being a strong suit of women. I wholeheartedly agree. And now more than ever, collaboration is important because, you know, the most powerful outcomes happen when the domain expertise, technical capability and strong judgment all come together to turn these intelligent insights into actual real life impacts and results. And then lastly, I would encourage women to really lean into the ownership of AI projects by doing, you know, using AI as much as possible, as I mentioned, using it in your personal and work life and just really getting comfortable with it.

Thanks, Tiffany. Emma, we touched earlier on kind of the speed of adoption and AI acceleration versus, you know, frameworks and governments. But, you know, kind of adjacent to that theme is how can women specifically differentiate themselves by leading in these areas and leading on responsible AI, governance and risk areas increasingly shaping the future of finance leadership?

Yeah. So being being that example. Right. And starting off and kicking it off. Right. So if that means encouraging your people to get up to speed and be comfortable with this, so thereby taking, you know, almost wearing an education hat and encouraging people to do that, then that's one way in which, you know, you're stepping up as a leader. The other is to encourage, you know, encouraging your team, encouraging your people to look at those big problems or the small ones. But in any case, the fundamental ones and how they can be solved.

And the other way in which, you know, you can you can step up is is evaluate. Right. Getting it. You know, I always say you have to go from cradle to grave from the very beginning to the end. Like how as a result of adopting AI in this specific fear and context, how how is that measured? What what does that measurement look like?

So always go to the end of the process and see, you know, how successful or not that that initiative was. And and it's it's it's showing that leadership and maturity saying that, look, we won't get it right all the time, but we're going to get it right and hopefully most of the time. So I think really understanding that I call it the three E's, which is the education, encouragement and evaluation.

And I think that's just leaders showing that they're very transparent and very accountable for what they're trying to do and not trying to, you know, hide behind complexity in any way. You know, bring it on. The more you have that, the more you get that fluency, you understand the risk layer more and more. You cross you know, you have that cross functional exposure, which is critical.

And then you're the translator. One of the most valuable skills and tools you can give in this sphere is being that translator between technical teams and business stakeholders. And that's what finance teams do all the time every day. But now it's just taking it another level.

Thank you. Wonderful. Again, we are wrapped on our topics and have a couple of minutes to get some questions to our panel. So I'm going to pick one question for each of our wonderful panels today. Tiffany, you know, you live and breathe this every day internally and across our customer base. And we have a question here about what how would you advise those people who are looking to make that first big step from being completely Excel wrapped to an AI roadmap, which is kind of alongside CPM.

I'm hoping I'm I'm hoping I'm articulating that. Actually, let me just read it for Adam. If you're still living in Excel, which we all do, what is the roadmap to introduce AI alongside trying to get cultural buy in for CPM?

That's a good question. I think many organizations still are Excel driven. So I can see why jumping straight into enterprise AI can feel daunting or unrealistic. But, you know, if you are living in Excel spreadsheets, as many organizations are today, I don't think the roadmap is, you know, rip and replace. I think it's phased and intentional.

So I would say first step is just to stabilize and standardize as much as you can. Right. Understand your metadata, consolidate your data, clean your data and then introduce a technology like a CPM solution as a foundation so that you are, you know, getting faster consolidations, getting a single source of truth, as I mentioned, with the data.

Reducing the manual processes that are happening in Excel. And then you layer on AI. And when we recommend layering on AI, we always say to our customers, look for the high value, low risk use cases. Right. There's going to be a department within your organization or a use case where, you know, the forecast takes an extremely long amount of time to create extremely manual or where there's a high variance in your actuals or forecasts. Find those use cases and get the data for that and then get the quick wins and then expand and scale from there.

And then, again, you can show impact quickly, get buy in very quickly and then start expanding from there. So really, you should just reframe AI, not as, you know, the project, as Shikha and Emma explained, but really as an augmentation and as a phased approach. This would be my recommendation.

Fantastic. A question for you, Shikha. Based on your experience and what you see in the market, what tools would you recommend to start looking into? And what tools have you found useful increasing productivity in your daily activities or for your team? Yeah, good question.

So without name dropping too many external tools, I would say that what I've done personally is kind of looked at the tools that I have at my disposal today and try to understand which ones have AI capability built into them, which ones have some sort of AI native

tool or module that I can kind of use to, you know, either summarize my daily tasks or consolidate information when information is in multiple places or run a model for me that I can then critically assess to see if that model kind of gives me the same conclusions that I was reaching myself. And so there are lots of tools that you probably use on a daily basis already.

Call it Excel, which Tiffany just talked about, or any of the other suite of products available to you that have some sort of AI capability built in. And so I would encourage you to start experimenting with those and figuring out which ones are, you know, interesting to experiment with versus actually changing how productive you are in a day, whether that is, again, summarizing information for you,

organizing your life a little bit, whether it's work or professionally. So that's where I started. Super simple. And then that gave me the confidence to kind of say, OK, now can I look at maybe AI tools themselves that kind of bridge some of these finance tools that I have that don't necessarily talk to each other, that I can then use sort of modernize finance more broadly.

Wonderful. Thanks, Sheikha. And then, Emma, I know you sit beside finance people, so you don't have to answer this from a finance point of view. But for those of us not yet using AI at all for finance, can you suggest a very specific first step, something that would help them build confidence?

You know, what is the mundane issue that you deal with? What is that bugbear that you have each day? We all have it. And so actually, it's about identifying what that is.

Once you identify what that is, you know, there are there are, you know, as mentioned already, there's tools you already have potentially.

So just just put that prompt in, just see just see what happens in terms of that issue that you have and start from there. It doesn't have to be necessarily a big moment in your head. Sometimes some some some feel that it has to be a big occasion for this to happen. It doesn't have to be. And I think we put a lot of pressure on ourselves with respect to that. And I day to day think of those issues and those problems and those things that are taking time out of my day where I could be using my brain for other questions.

And I put it in and I see what comes out. And I think it's just taking that first step. But it's about identifying that that issue that you have, that niggly thing that's that's annoying you day to day that can save you time. What is that? And then take it from there. I love it. And I reckon of the 250 plus people on this call, every single person has at least one of those examples that they wish they could take off their day or their week or their month or their quarter. And OK, so very close to end time. So I want to do like a quick fire round. Knowing what you know now.

And what would you have done differently or told yourself at the start of your A.I. journeys? Shika, we'll go with you first. Thanks.

It was lovely to be here. So thank you for inviting me. I would have asked earlier what A.I. means for me. So, you know, we all had experiences with like chatbots or or robotic process automation. This was a decade ago or more. And I would have asked a lot earlier. What does what does a chatbot mean for me and my future in finance?

Excellent. Tiffany, you next, please. Yeah, I would have actually told myself I would have stopped waiting to feel technical enough early in my career. So I always had this mental model where there was a threshold of technical knowledge that I needed to cross before I could credibly lead or own projects or work streams.

So I really would have stopped second guessing whether I knew enough or whether my background was technical enough. All those doubts in my head. And so if anything, this A.I. revolution has taught me is that everyone is learning. And as long as you stay curious and get your hands dirty, you'll stay ahead.

Fantastic. Emma, over to you. And don't ever stop thinking of challenges and problems and issues. Right. We're surrounded by them every day. It's what we do. And I think what I what I would have done differently is not be afraid to relay those those issues more and spending more time thinking about the challenges and the issues so that you'd ask yourself even more questions. Wonderful.

And I want to extend a huge, huge thanks to each of you. Shikha, Tiffany, Emma, this has been incredibly valuable. And I find my own kind of brain worrying as each of you speak, because I'm thinking of where I am on my own journey and how quickly I could have jumped in. And should I have jumped any quicker and get my hands dirty, as Tiffany said. So thank you to each of you so much for those on the call joining us on the webinar today. Thank you for joining us.

Please check out our LinkedIn page or our LinkedIn group. One Women in Finance, the one stream women in finance group where you can keep up to date on latest think pieces, upcoming events, upcoming webinars. But for today, thank you so much to each of you. Thanks so much, Ashley.